import hashlib
import os
import tarfile
import zipfile
import requests

#@save
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'
def download(name, cache_dir=os.path.join('..', 'data')):  #@save
    """下载一个DATA_HUB中的文件，返回本地文件名"""
    assert name in DATA_HUB, f"{name} 不存在于 {DATA_HUB}"
    url, sha1_hash = DATA_HUB[name]
    os.makedirs(cache_dir, exist_ok=True)
    fname = os.path.join(cache_dir, url.split('/')[-1])
    if os.path.exists(fname):
        sha1 = hashlib.sha1()
        with open(fname, 'rb') as f:
            while True:
                data = f.read(1048576)
                if not data:
                    break
                sha1.update(data)
        if sha1.hexdigest() == sha1_hash:
            return fname  # 命中缓存
    print(f'正在从{url}下载{fname}...')
    r = requests.get(url, stream=True, verify=True)
    with open(fname, 'wb') as f:
        f.write(r.content)
    return fname

def download_extract(name, folder=None):  #@save
    """下载并解压zip/tar文件"""
    fname = download(name)
    base_dir = os.path.dirname(fname)
    data_dir, ext = os.path.splitext(fname)
    if ext == '.zip':
        fp = zipfile.ZipFile(fname, 'r')
    elif ext in ('.tar', '.gz'):
        fp = tarfile.open(fname, 'r')
    else:
        assert False, '只有zip/tar文件可以被解压缩'
    fp.extractall(base_dir)
    return os.path.join(base_dir, folder) if folder else data_dir

def download_all():  #@save
    """下载DATA_HUB中的所有文件"""
    for name in DATA_HUB:
        download(name)

import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l

device = torch.cuda.current_device()
print(device)

DATA_HUB['kaggle_house_train'] = (  #@save
    DATA_URL + 'kaggle_house_pred_train.csv',
    '585e9cc93e70b39160e7921475f9bcd7d31219ce')

DATA_HUB['kaggle_house_test'] = (  #@save
    DATA_URL + 'kaggle_house_pred_test.csv',
    'fa19780a7b011d9b009e8bff8e99922a8ee2eb90')

train_data = pd.read_csv(download('kaggle_house_train'))
test_data = pd.read_csv(download('kaggle_house_test'))
# train_data = pd.read_csv('D:\\PytorchLearn\\pytorch_learning\\Project1\\data\\train.csv')
# test_data = pd.read_csv('D:\\PytorchLearn\\pytorch_learning\\Project1\\data\\test.csv')

# print(train_data.shape)
# print(test_data.shape)
# #
# print(train_data.iloc[0:4,[0,1,2,3,-3,-2,-1]])
# print(test_data.iloc[0:4,[0,1,2,3,-3,-2,-1]])

all_features = pd.concat((train_data.iloc[:,1:-1],test_data.iloc[:,1:-1]),axis=0) # 去除第一列并合并,并且和测试集保持一致，将saleprice留下
#
numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
all_features[numeric_features] = all_features[numeric_features].apply(  # 均值为0 方差为1
    lambda x :(x-x.mean())/(x.std()))
all_features[numeric_features] = all_features[numeric_features].fillna(0)  #把 nan变为0

all_features = pd.get_dummies(all_features,dummy_na=True)
numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
all_features[numeric_features]=all_features[numeric_features]==True
#
n_train = train_data.shape[0]
train_features = torch.tensor(all_features[:n_train].values, dtype=torch.float32)
test_features = torch.tensor(all_features[n_train:].values, dtype=torch.float32)
train_labels = torch.tensor(train_data.SalePrice.values.reshape(-1, 1), dtype=torch.float32)

loss = nn.MSELoss()
in_features = train_features.shape[1]
print(train_features.shape)

def get_net():
    net = nn.Sequential(nn.Linear(in_features,1))

    return net

def log_rmse(net,features,labels):
    clipped_preds = torch.clamp(net(features),1,float('inf'))
    rmse = torch.sqrt(loss(torch.log(clipped_preds),torch.log(labels)))
    return rmse.item()

def train(net,train_features,train_labels,test_features,test_labels,num_epochs,lr,weight_decay,batch_size):
    train_ls,test_ls = [],[]
    train_iter = d2l.load_array((train_features,train_labels),batch_size)
    optimizer = torch.optim.Adam(net.parameters(),lr=lr,weight_decay=weight_decay)
    for epoch in range(num_epochs):
        for X,y in train_iter:
            optimizer.zero_grad()
            l = loss(net(X),y)
            l.backward()
            optimizer.step()
        train_ls.append(log_rmse(net,train_features,train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(net,test_features,test_labels))
    return train_ls,test_ls

def get_k_fold_data(k,i,X,y):
    assert k>1
    fold_size = X.shape[0] // k
    X_train,y_train = None,None
    for j in range(k):
        # idx = slice(j*fold_size,(j+1)*fold_size)
        X_part,y_part = X[j*fold_size:(j+1)*fold_size,:],y[j*fold_size:(j+1)*fold_size]
        if j == i:
            X_valid,y_valid =X_part,y_part
        elif X_train == None:
            X_train ,y_train = X_part,y_part
        else:
            X_train = torch.cat([X_train,X_part],dim=0)
            y_train = torch.cat([y_train,y_part],dim=0)
    return X_train,y_train,X_valid,y_valid

def k_fold(k,X_train,y_train,num_epochs,lr,weight_decay,batch_size):
    train_l_sum , valid_l_sum = 0,0
    for i in range(k):
        data = get_k_fold_data(k,i,X_train,y_train)
        net = get_net()
        train_ls,valid_ls = train(net,*data,num_epochs,lr,weight_decay,batch_size)
        train_l_sum += train_ls[-1]
        valid_l_sum += valid_ls[-1]
        if i == 0:
            d2l.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls],
                     xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs],
                     legend=['train', 'valid'], yscale='log')
        print(f'折{i + 1}，训练log rmse{float(train_ls[-1]):f}, '
              f'验证log rmse{float(valid_ls[-1]):f}')
    return train_l_sum / k, valid_l_sum / k

k ,num_epochs,lr,weight_decay,batch_size = 30,100,3,0,64
train_l ,valid_l = k_fold(k,train_features,train_labels,num_epochs, lr, weight_decay, batch_size)
print(f'{k}折验证：平均训练log rmse:{float(train_l):f}\t平均验证log rmse{float(valid_l):f}')
d2l.plt.show()
#
# def train_and_pred(train_features,test_features,train_labels,test_data,num_epochs,lr,weight_decay,batch_size):
#     net = get_net()
#     train_ls,_=train(net,train_features,train_labels,None,None,num_epochs,lr, weight_decay, batch_size)
#     d2l.plot(np.arange(1,num_epochs+1),[train_ls],xlabel='epoch',
#              ylabel='log rmse',xlim=[1,num_epochs],yscale='log')
#     print(f"train log rmse{float(train_ls[-1]):f}")
#     preds = net(test_features).detach().numpy()
#     test_data['SalePrice']=pd.Series(preds.reshape(1,-1)[0])
#     submission = pd.concat([test_data['Id'],test_data['SalePrice']],axis=1)
#     submission.to_csv('submission.csv',index=False)
#
# train_and_pred(train_features,test_features,train_labels,test_data,num_epochs=num_epochs,lr=lr,weight_decay=weight_decay,batch_size=batch_size)

